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Register a new userA Framework for Network AB Testing
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Added by
Bai Jiang
Publication date
2016
fields
Mathematical Statistics
and research's language is
English
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A/B testing, also known as controlled experiment, bucket testing or splitting testing, has been widely used for evaluating a new feature, service or product in the data-driven decision processes of online websites. The goal of A/B testing is to estimate or test the difference between the treatment effects of the old and new variations. It is a well-studied two-sample comparison problem if each users response is influenced by her treatment only. However, in many applications of A/B testing, especially those in HIVE of Yahoo and other social networks of Microsoft, Facebook, LinkedIn, Twitter and Google, users in the social networks influence their friends via underlying social interactions, and the conventional A/B testing methods fail to work. This paper considers the network A/B testing problem and provide a general framework consisting of five steps: data sampling, probabilistic model, parameter inference, computing average treatment effect and hypothesis test. The framework performs well for network A/B testing in simulation studies.
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This paper makes the following original contributions. First, we develop a unifying framework for testing shape restrictions based on the Wald principle. The test has asymptotic uniform size control and is uniformly consistent. Second, we examine the applicability and usefulness of some prominent shape enforcing operators in implementing our framework. In particular, in stark contrast to its use in point and interval estimation, the rearrangement operator is inapplicable due to a lack of convexity. The greatest convex minorization and the least concave majorization are shown to enjoy the analytic properties required to employ our framework. Third, we show that, despite that the projection operator may not be well-defined/behaved in general parameter spaces such as those defined by uniform norms, one may nonetheless employ a powerful distance-based test by applying our framework. Monte Carlo simulations confirm that our test works well. We further showcase the empirical relevance by investigating the relationship between weekly working hours and the annual wage growth in the high-end labor market.
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